Replication of below article’s Data and Visualizations
“We keep pumping money into the NHS. Is it good value?”
By Tom Calver

Karim K. Kardous

#|echo: false
#|message: false
#|warning: false
#|include: false

# install pacman if it's not already installed
if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
# install.packages("gdtools", type = "source")

## NOTE; potential (might not be needed) steps on mac for registering then loading Roboto font into Quarto below

## from terminal/shell
# brew install cairo fontconfig freetype pkg-config
# export PKG_CONFIG_PATH="/opt/homebrew/lib/pkgconfig:/opt/homebrew/share/pkgconfig"
# export PKG_CFLAGS="-I/opt/homebrew/include"
# export PKG_LIBS="-L/opt/homebrew/lib"

## then from Rstudio
# install.packages("gdtools", type = "source")



# Load or install packages
pacman::p_load(
  gdtools,
  tidyverse,
  quarto,
  chromote,
  here,
  tidycensus,
  janitor,
  purrr,
  ggtext,
  ggiraph,
  gfonts,
  showtext,
  ggborderline,
  shiny,
  gt,
  rsvg,
  magick,
  stringr,
  ggimage
)

# Set theme and options
options(scipen = 999)
theme_set(theme_minimal())

suppressMessages({
  gdtools::register_gfont("Roboto", "roboto")
  sysfonts::font_add_google("Roboto", "roboto")
  showtext_auto()
  showtext_opts(dpi = 300)
})

Data Import/Read: Lucky for us, the data is embedded in

```{r}
#| message: false
#| output: false
#| warning: false
b <- ChromoteSession$new()
b$Page$navigate("https://www.thetimes.com/comment/columnists/article/we-keep-pumping-money-into-the-nhs-is-it-good-value-blq8bxc39")
Sys.sleep(6) # allow some time for dynamic content to render

# extract all iframe srcs (joined by || in this case)
iframes_html <- b$Runtime$evaluate("Array.from(document.querySelectorAll('iframe')).map(el => el.src).join('||')")$result$value
# split and filter valid Datawrapper url's
chart_urls <- str_split(iframes_html, "\\|\\|")[[1]] |>
  str_subset("^https://datawrapper\\.dwcdn\\.net/[a-zA-Z0-9]+/\\d+$")

all_data <- purrr::map_dfr(chart_urls, function(url) {  
  message("Navigating to: ", url)
  b$Page$navigate(url)
  Sys.sleep(3)
  
  html <- b$Runtime$evaluate("document.documentElement.outerHTML")$result$value
  
  # match visible chart values if any
  pattern <- 'aria-datavariables="year,\\s*([A-Z]+)".*?aria-datavalues="([0-9]{4}),\\s*([0-9.]+)"'
  matches <- str_match_all(html, pattern)[[1]]
  
  # match dataset.csv url as well 
  csv_pattern <- "https://datawrapper\\.dwcdn\\.net/[a-zA-Z0-9]+/\\d+/dataset\\.csv"
  csv_link <- str_extract(html, csv_pattern)
  if (is.na(csv_link)) {
    csv_link <- str_glue("{url}/dataset.csv")
  }
  
  tibble(
    chart_url = url,
    country = if(nrow(matches)) matches[, 2] else NA,
    year = if(nrow(matches)) as.integer(matches[, 3]) else NA,
    value = if(nrow(matches)) as.numeric(matches[, 4]) else NA,
    dataset_csv = csv_link
  )
})
```

# add a custom gt boilerplate -from {gt} package (great tables)-to reduce code redundancy (having to copy/paste same chunks of code every # time we turn a tibble into a gt object)

gt_nyt_custom <- function(x, title = '', subtitle = '', first_10_rows_only = TRUE){
  
  x <- x |> clean_names(case = 'title')
  numeric_cols <- x |> select(where(is.double)) |> names()
  integer_cols <- x |> select(where(is.integer)) |> names()
  
  title_fmt <- if(title != "") glue::glue("**{title}**") else ""
  subtitle_fmt <- if(subtitle != "") glue::glue("*{subtitle}*") else ""
  
  x |>
    (\(x) if (first_10_rows_only) slice_head(x, n = 10) else x)() |>
    gt() |> 
    tab_header(
      title = md(title_fmt),
      subtitle = md(subtitle_fmt)
    ) |> 
    tab_style(
      style = list(
        cell_text(color = '#333333')
      ),
      locations = cells_body()
    ) |> 
    tab_style(
      style = list(
        cell_text(color = '#CC6600', weight = 'bold')
      ),
      locations = cells_column_labels(everything())
    ) |> 
    fmt_number(
      columns = c(numeric_cols),
      decimals = 1
    ) |> 
    fmt_number(
      columns = c(integer_cols),
      decimals = 0
    ) |> 
    tab_options(
      table.font.names = c("Merriweather", "Georgia", "serif"),
      table.font.size = 14,
      heading.title.font.size = 18,
      heading.subtitle.font.size = 14,
      column_labels.font.weight = "bold",
      column_labels.background.color = "#eeeeee",
      table.border.top.color = "#dddddd",
      table.border.bottom.color = "#dddddd",
      data_row.padding = px(6),
      row.striping.include_table_body = TRUE,
      row.striping.background_color = "#f9f9f9"
    )
  
}
# display 
all_data |>
  count(
    url = chart_url, download_link = dataset_csv
  ) |>
  select(-n) |>
  gt_nyt_custom(
    title = 'Dataset Ids'
  ) |>
  cols_label(
    Url = "Plot URL",
    `Download Link` = "Link to CSV"
  ) |> 
  tab_footnote(
    "Again, in the event you download the links yourself and run your own script,
    the last two should be treated as tsv files, otherwise csv's"
  ) 

Dataset Ids
Plot URL	Link to CSV
https://datawrapper.dwcdn.net/7NJRB/1	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/Bxhol/4	https://datawrapper.dwcdn.net/Bxhol/4/dataset.csv
https://datawrapper.dwcdn.net/JH3Qn/1	https://datawrapper.dwcdn.net/JH3Qn/1/dataset.csv
https://datawrapper.dwcdn.net/Mc3q2/2	https://datawrapper.dwcdn.net/Mc3q2/2/dataset.csv
https://datawrapper.dwcdn.net/eXQPs/1	https://datawrapper.dwcdn.net/eXQPs/1/dataset.csv
Again, in the event you download the links yourself and run your own script, the last two should be treated as tsv files, otherwise csv's

health_sat <- 
  all_data |> 
  drop_na() |> 
  mutate(
    country_name = case_when(
      country == "DE" ~ "Germany",
      country == "ES" ~ "Spain",
      country == "FR" ~ "France",
      country == "GB" ~ "UK",
      country == "IE" ~ "Ireland",
      country == "NO" ~ "Norway",
      country == "PT" ~ "Portugal",
      TRUE ~ NA_character_
    )
  )

Health service satisfaction (ESS Survey; European Social Survey)

# now we can focus on building plot 
all_data |> 
  gt_nyt_custom() |> 
  tab_header(
    title = md("**Chart Data Summary**"),
    subtitle = md("*Extracted from embedded datawrapper from the HTML Source page*")
  )

Chart Data Summary
Extracted from embedded datawrapper from the HTML Source page
Chart Url	Country	Year	Value	Dataset Csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,002	4.8	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,004	4.7	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,006	4.4	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,008	4.6	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,010	4.8	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,012	5.7	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,014	5.9	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,016	6.2	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,018	5.9	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv
https://datawrapper.dwcdn.net/7NJRB/1	DE	2,020	5.9	https://datawrapper.dwcdn.net/7NJRB/1/dataset.csv

# let's just go with smoothing 
extract_smooth_build <- function(tibble, country = 'GB'){
  
  initial_pull <- 
    all_data |> 
    filter(country %in% {{country}}) |> 
    ggplot(aes(x = year, y = value)) + 
    geom_smooth(method = 'loess')
  
  # fetch country abbs for ids, and ranges
  country_ids <- c(na.omit(all_data |> pull(country) |> unique()))
  country_max <- all_data |> filter(country == {{country}}) |> pull(value) |> max()
  country_min <- all_data |> filter(country == {{country}}) |> pull(value) |> min()
  
  # access smoothed, include actual years to imputed/smoothed points, cap at min max per country/series
  # and keep only columns of interest
  smoothed_df <- ggplot_build(initial_pull)[[1]] |> as.data.frame() |> as_tibble()
  
  complete_series <- 
    smoothed_df |> 
    select(year = x, value = y) |> 
    mutate(country := country) |> 
    bind_rows(
      all_data |> 
        filter(country == {{country}}) |> 
        select(year, value) 
    ) |> 
    mutate(
      year = as.integer(year),
      year_val_tie_breaker = if_else(is.na(country), 1, 0)
    ) |> 
    group_by(country, year) |> 
    arrange(desc(year_val_tie_breaker)) |> 
    mutate(ties = row_number()) |> 
    filter(
      if (n() < 4) TRUE else ties + year_val_tie_breaker != 1 # make sure every year/country combo gets same no. of obs
      # and that original values (only in the event a given year is even or survey year) take precedence over smoothed ones
      # otherwise just pass/do nothing
    ) |> 
    ungroup() |> 
    # ensuer smoothed values don't go below/beyond lower/upper bounds
    mutate(
      value = pmin(pmax(value, country_min), country_max)
    ) |> 
    arrange(year) |> 
    fill(country, .direction = 'downup') |>  # since every year starts with 
    select(year, country, value) 
  
  return(complete_series)
  
}
# country vector to loop thru
country_name_abbs <- c(na.omit(all_data |> pull(country) |> unique()))
# combine all series
all_series <- map_dfr(.x = country_name_abbs, ~extract_smooth_build(tibble = all_data, country = .x))

# set contry 'switch; so that tooltip can change accordingly for odd numebred years
country_labels <- c(
  NO = "Norway", DE = "Germany", ES = "Spain",
  FR = "France", GB = "UK", IE = "Ireland", PT = "Portugal"
)

# adding year as continuous variable (decimal years) so that points don't overlap but strech over whithin a year to year span
all_series <- 
  all_series |> 
  mutate(
    rn = row_number(), .by = c(country, year)
  ) |> 
  mutate(
    decimal_year = if_else(rn == 1, year, year + rn / 8)
  ) |> 
  mutate(
    year = decimal_year
  ) |> 
  select(-decimal_year)

# also join on country full name mapping so we can generate a consolidate data_id that links the aestethics together (for interactive simultaneus highlighting, etc.)
all_series <- 
  all_series |> 
  inner_join(
    country_labels |> enframe() |> rename(values = value), 
    join_by(country == name)
  ) |> 
  mutate(
    data_id = str_c(country, values)
  ) |> 
  select(-values) |> 
  mutate(
    country_name = str_sub(data_id, 3, 20)
  )

# also generate visible (and non visible years by exclusion) as they don't visually get the same properties; visible (even numbered years) get the country abb as a tooltip (and larger markers/circles), 
# while 'invisible' ones (odd numbered years along with year 2023) get their country names fully spelled out and get transparent marker/circle fill
visible_years <- c(seq(2002, 2022, 2), 2023)

visible_points <- 
  all_series |> 
  filter(round(year) %in% visible_years & floor(year) == ceiling(year))
invisible_points <- all_series |> 
  filter(!round(year) %in% visible_years & floor(year) != ceiling(year))

# final touchups
# set color mappings
color_map <- expr(
  case_when(
    country %in% c('NO', 'Norway') ~ '#d43b45',
    country %in% c('DE', 'Germany') ~ '#DCA825',
    country %in% c('ES', 'Spain') ~ '#b01622',
    country %in% c('FR', 'France') ~ '#487caa',
    country %in% c('GB', 'UK') ~ '#264250',
    country %in% c('IE', 'Ireland') ~ '#61A861',
    country %in% c('PT', 'Portugal') ~ '#d27e4e',
    TRUE ~ '#000000'
  )
)

# set tooltip mappings
tooltip_map <- expr(
  case_when(
    !year %in% c(seq(2002, 2022, 2), 2023) & country %in% names(country_labels) ~ country_labels[country],
    TRUE ~ country
  )
)

label_data <-
  all_series |>
  group_by(country) |>
  arrange(desc(year)) |> 
  filter(row_number() == 1) |>
  mutate(
    y_offset = case_when(
      country == 'ES' ~ value + .1,
      country == 'FR' ~ value +  0,
      country == 'DE' ~ value - .05,
      country == 'GB' ~ value - .1,
      country == 'PT' ~ value + .2,
      TRUE ~ value)
  ) |> 
  ungroup() |> 
  mutate(
    country_name = case_when(
      country == "DE" ~ "Germany",
      country == "ES" ~ "Spain",
      country == "FR" ~ "France",
      country == "GB" ~ "UK",
      country == "IE" ~ "Ireland",
      country == "NO" ~ "Norway",
      country == "PT" ~ "Portugal",
      TRUE ~ NA_character_
    ),
    country_color = case_when(
      country %in% c("DE", 'Germany') | country_name %in% 'Germany' ~ "#9b6e00",  # override DE/Germany label color here since curve color is different than country label color (only one)
      country %in% c('NO', 'Norway') ~ '#d43b45',
      country %in% c('ES', 'Spain') ~ '#b01622',
      country %in% c('FR', 'France') ~ '#487caa',
      country %in% c('GB', 'UK') ~ '#264250',
      country %in% c('IE', 'Ireland') ~ '#61A861',
      country %in% c('PT', 'Portugal') ~ '#d27e4e',
      TRUE ~ '#000000'
    )
  ) |> 
  inner_join(
    country_labels |> enframe() |> rename(values = value), join_by(country == name)
  ) |> 
  mutate(
    data_id = str_c(country, values),
    country = if_else(country == 'DE', 'Germany', country)
  )
# add caption to match Tom's
caption_text <- "<span style='color:#232323;'>0 = extremely bad, 10 = extremely good.</span><br>
                 <span style='color:#939293; font-weight: bold;'>Chart: Tom Calver | The Times and The Sunday Times • Source: ESS/K. Kardous</span><br>"

p <- 
  all_series |> distinct() |> 
  ggplot(
    aes(x = year, 
        y = value, 
        group = data_id, 
        color = country)
  ) +
  scale_color_manual(
    values = c(
      'NO' = "#d43b45",
      'DE' = '#DCA825',
      'ES' = '#b01622',
      'FR' = '#487caa',
      'GB' = '#264250',
      'IE' = '#61A861',
      'PT' = '#d27e4e')
  ) +
  scale_y_continuous(breaks = seq(0, 7, 1), limits = c(0, 8)) + 
  scale_x_continuous(
    breaks = seq(2002, 2022, 2), 
    limits = c(2002, 2023),
    expand = c(0, 0.1)
  ) + 
  theme(
    legend.position = 'none',
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  geom_smooth_interactive(
    data = all_series,
    aes(x = year, y = value, data_id = paste0(country, country_name)),
    method = "loess",
    se = FALSE,
    linewidth = 3.5, # thick line acts as the 'border'
    alpha = 1,
    show.legend = FALSE,
    color = "white"
  ) +
  # colored interactive smooth line
  geom_smooth_interactive(data = all_series |> filter(!country %in% 'IE'),
                          aes(data_id = paste0(country, country_name)),
                          method = "loess", 
                          se = FALSE, 
                          linewidth = 0.9, 
                          fill = NA
  ) +
  geom_smooth_interactive(data = all_series |> filter(country %in% 'IE'),
                          aes(data_id = paste0(country, country_name)),
                          method = "loess", se = FALSE, linewidth = 0.9, fill = NA
  ) +
  scale_y_continuous(breaks = seq(0, 7, 1), limits = c(0, 8)) + 
  scale_x_continuous(
    breaks = seq(2002, 2022, 2), 
    limits = c(2002, 2024),
    expand = c(0, 0.1)
  ) +
  labs(
    x = NULL,
    y = NULL,
    caption = caption_text
  ) +
  # final touchoups before interactive rendering thru girafe()
  theme(
    panel.spacing = unit(20, 'cm'),
    plot.margin = margin(l = 5, b = 10), # leave some space/margin at the bottom for caption 'room to breathe'
    legend.position = 'none',
    axis.text = element_text(face = "bold"), # axis tick labels
    strip.text = element_text(face = "bold"), # facet labels
    panel.grid.major.x = element_blank(),
    axis.text.x = element_text(margin = margin(b = 9, t = -9)),
    panel.grid.major.y = element_line(color = "gray90"),
    plot.caption = element_markdown(
      hjust = 0,
      size = 9,
      lineheight = 1.4,
      family = "roboto",
      face = 'bold',
      margin = margin(l = -10, t = 5)
    ) 
  ) +
  geom_segment(aes(x = 2002, xend = 2023, y = 0, yend = 0), color = 'black')

p_interactive <- p +
  geom_point_interactive(
    data = visible_points,
    aes(
      x = year,
      y = value, 
      color = country,
      data_id = paste0(country, country_name)
    ),
    alpha = 0.1, fill = 'white', show.legend = FALSE
  ) +
  geom_point_interactive(
    data = 
      all_series |> 
      mutate(
        point_size = if_else(country %in% c('NO', 'Norway', 'PT', 'Portugal'), 3, 1.5),
        point_stroke = point_size
      ),
    aes(
      x = year, 
      y = value,
      data_id = paste0(country, country_name),
      tooltip = paste0(
        "<div style='text-align:", 
        if_else(year <= 2015.250, "left", "right"), 
        "; line-height: 1.1;'>", # tightens spacing
        "<div style='font-weight:bold; font-size:16px; color:",
        if_else(country_name == "Germany", "#9b6e00", eval(color_map)), 
        ";'>", 
        eval(tooltip_map), 
        "</div>",
        "<div style='font-size:16px;'>", round(year, 0), "</div>",
        "<div style='font-size:16px;'>", round(value, 2), "</div>",
        "</div>"
      )
    ),
    color = 'white', fill = 'white', shape = 21, alpha = 0, show.legend = FALSE
  ) +
  geom_rect(
    inherit.aes = FALSE,
    aes(xmin = 2024, xmax = Inf, ymin = -Inf, ymax = Inf),
    color = NA, fill = "white", 
  ) +
  geom_label_interactive(
    data = all_series |> slice_max(year) |> mutate(country_name = str_sub(data_id, 3, 20)),
    aes(
      x = year,
      y = value,
      group = paste0(country, country_name),
      label = country_name,
      data_id = paste0(country, country_name)
    ),
    label.size = NA,
    fill = NA,
    size = 2.2,
    hjust = 0,
    fontface = 'bold',
    inherit.aes = TRUE,
    alpha = 1
  ) +
  scale_color_manual(
    breaks = c("GB", "FR", "IE", "PT", "ES", "NO", 'DE', 'Germany'), # this makes sure Germany as a country label gets dark yellow while 'DE' as a curve gets a darker yellow color
    values = c("#264250", "#487caa", "#61A861", "#d27e4e", "#b01622", "#d43b45", '#DCA825', '#9b6e00') # this makes sure Germany as a country label gets dark yellow while 'DE' as a curve gets a darker yellow color
  ) +
  # scale_color_identity() +  # correctly apply the country color to the label's font
  coord_cartesian(xlim = c(2002, 2024.5)) +
  theme(
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  # add persistent white circle that follows mouse (via selection); one to cover all data points is simply assigning data to country.year combo
  geom_point_interactive(
    data = all_series,
    aes(
      x = year,
      y = value,
      group = paste0(year, country_name)
    ),
    shape = 21,
    size = 0.4,
    stroke = 1,
    fill = 'white',
    color = "grey85",
    alpha = 0
  ) 
# render interactive plot thru girafe() engine
girafe(
  ggobj = p_interactive,
  options = list(
    opts_tooltip(
      css = "
      background: transparent;
      border: none;
      box-shadow: none;
      font-family: sans-serif;
      text-shadow:
      0 0 4px rgba(234, 255, 255, 1),
      0 0 4px rgba(234, 255, 255, 1),
      0 0 4px rgba(255, 255, 255, 1);
      border-radius: none;
      transform: translate(-50%, 20px);
      transition: all 0.2s ease-in-out;",
      delay_mouseover = 300,
      delay_mouseout = 500
    ),
    opts_hover(
      css = "stroke-width: 3; stroke-opacity: 0.9; fill-opacity: 0.9; opacity: 1;",
      nearest_distance = 30,
      reactive = FALSE
    ),
    opts_hover_inv(
      css = "stroke-opacity: 0.2; fill-opacity: 0.2; opacity: 0.4;"
    )
  )
)

Room to improve

# generate the data
p3_prep <-
  tribble(
    ~Current,  ~Potential,  ~Country,
    78.9,      84.1,       "United States",
    78.1,      83.8,       "Germany",
    80.9,      80.9,       "Poland",
    81.1,      83.8,       "United Kingdom",
    81.6,      84.1,       "Ireland",
    81.4,      83.8,       "Finland",
    82.0,      84.1,       "Norway",
    82.3,      84.1,       "France",
    82.4,      84.1,       "Sweden",
    81.4,      82.6,       "Portugal",
    83.0,      84.1,       "Australia",
    83.0,      83.2,       "Italy",
    81.7,      81.7,       "Greece",
    83.1,      83.1,       "Spain",
    84.1,      84.1,       "Japan"
  ) |> 
  select(last_col(), everything()) |> 
  # order gets lost when pivoting longer so we create a integer sequence and force rank/arrange that way
  mutate(seq = 1:15)

# create country abb names (fetched from one of the html nodes within the original plot) and build urls/per flag
country_abbs <- c('us', 'de', 'pl', 'gb', 'ie', 'fi', 'no', 'fr', 'se', 'pt', 'au', 'it', 'gr', 'es', 'jp')
flag_urls <- str_glue("https://static.dwcdn.net/css/flag-icons/flags/4x3/{country_abbs}.svg")

# loop thru svg's and convert to png's
flag_paths <- map(
  flag_urls, function(url) {

    svg_path <- tempfile(fileext = ".svg")
    png_path <- tempfile(fileext = ".png")
    
    download.file(url, svg_path, mode = "wb")
    rsvg::rsvg_png(svg_path, png_path)
    
    return(png_path)
  }
) 
flag_paths <- setNames(flag_paths, country_abbs)


# add the 2 additional columns back to p3
p3_data <- p3_prep |> 
  bind_cols(
    flag_paths |> unlist() |> stack() |> rename(flag_pngs = values, country_abbs = ind)
  ) |> 
  select(Country, country_abbs, Current, Potential, flag_pngs)

# also add html code straight into p3_data but first abbreviate country names
p3_data <- 
  p3_data |> 
  # abbreviated United States and United Kingdom because i noticed the blanks/two or more words can throw off element markdown, especially
  # when embedding svg's; while not perfectly replicating here, in the context of country names, 'US' and 'UK' are universally reconized, especially
  # if flag images are appended to them 
  mutate(
    Country = if_else(
      Country == 'United Kingdom', 'UK',
      if_else(Country == 'United States', 'US', Country)
      ),
    flag_html = sprintf("<img src='%s' width='25' height='15'> %s", flag_pngs, Country)
    )
# we will also preserve html flags as a single charazcter string in the event we create an independ plot and stack it (veritically)
# alongside progress plot (for Current vs. Potential arrow chart)
# flag_html <- str_c(
#   map2(rep(flag_paths, each = 2), p3_data_prep$Country,
#            ~ sprintf("<img src='%-s' width='20' height='15'> %-s", .x, .y)),
#   collapse = "<br>"
# )

# we have to turb this from wide to long; to get a tracking per country (current -> potential)
p3_data_prep <- 
  p3_data |> 
  # order gets lost when pivoting longer so we create a integer sequence and force rank/arrange that way
  mutate(seq = 1:15) |> 
  pivot_longer(
    -c(seq, Country, country_abbs, flag_pngs, flag_html), names_to = 'progress'
  ) |> 
  mutate(
    # add color codes (different for UK compared to rest)
    # for greece, default arrow shows a recession, but in Tom's plot, it's '>', it's the same value for current and for potential for Greece, so we 
    # artificially add + 0.001 to the Greek score for potential for force '>' arrow direction
    # value = if_else(Country == 'Greece' & progress == 'Potential', value + 0.0001, value),
    hex_codes = if_else(Country == 'UK', '#73a3d3', '#264250'), # UK gets its own color
    arrow_end_angle = if_else(Country %in% c('Spain', 'Japan'), 90, 70)) |> 
  arrange(desc(seq)) # for some reason, below plot was reversing order, so we reverse order here so that plot arranges countries properly

# since arrow() wouldn't natively recoznied arrow_end_angle,
# we create an variable in the global env. to call it within arrow() later on
p3 <- p3_data_prep |> 
  ggplot(aes(y = fct_reorder(Country, -seq), x = value, color = hex_codes)) +  
  geom_path(arrow = arrow(
    type = "open", 
    angle = c(rep(90, 3), rep(60, 27)), 
    length = unit(3, 'pt')), 
    linewidth = .8
  ) +
  geom_label(
    data = p3_data_prep |> filter(progress == 'Current'),
    aes(label = value, hjust = 1.2, family = "roboto"),
    size = 2, fill = 'white', label.size = NA
  ) +
  geom_text(
    data = p3_data_prep |> filter(progress == 'Potential'),
    aes(label = value, hjust = -.3, family = "roboto"),
    size = 2
  ) + 
  geom_text(
    aes(x = 78.9, y = 15, label = '\nCurrent\n', family = 'roboto'), 
    size = 1.9, nudge_y = .5, nudge_x = -.2
  ) + 
  geom_text(
    aes(x = 84.1, y = 15, label = '\nPotential\n', family = 'roboto'), 
    size = 1.9, nudge_y = .5, nudge_x = .1
  ) + 
  scale_color_identity() +  
  scale_y_discrete(
    labels = p3_data_prep |> filter(progress == "Current") |> pull(flag_html)
  ) +
  theme(
    axis.text.y = element_markdown(inherit.blank = FALSE, family = "roboto", size = 4, hjust = 0, face = 'bold'),  
    axis.text.x = element_text(family = "roboto", size = 4),  
    axis.title.x = element_text(size = 7),  
    axis.title.y = element_text(size = 7), 
    panel.grid.minor.x = element_blank()
    # plot.margin = margin(r = 30)
  ) + 
  labs(x = NULL, y = NULL) + 
  theme(legend.position = 'none') + 
  geom_segment(
    aes(x = 78.9, xend = 78.9, y = 15.1, yend = 15.3), 
    color = "grey70", linewidth = 0.1, inherit.aes = FALSE
  ) + 
  geom_segment(
    aes(x = 84.1, xend = 84.1, y = 15.1, yend = 15.3), 
    color = 'grey70', linewidth = 0.1, inherit.aes = FALSE
  ) 

# add plot caption to match what Tom has 
caption_text <- "<span style='color:#989799; font-weight:bold;'>Chart: The Times And The Sunday Times • Source</span> 
                 <span style='color:#232323; font-weight:bold;'>Zarulli et al.</span>"

p3 <- p3 + 
  labs(caption = caption_text) + 
  theme(
    plot.caption = element_markdown(inherit.blank = TRUE,
                                    hjust = -0.18,  
                                    size = 4.7,
                                    lineheight = 1.2,
                                    family = "roboto"
    )
  )
p3

Value for money (Excludes 2020-22 given the high COVID spend)

country_labels <- tribble(
  ~country, ~year, ~spend, ~life_expectancy, ~xnudge, ~ynudge,
  "France", 2023,  5014,   83.3,             500,       0,
  "Germany",2023,  5971,   81.4,             600,       0,
  "UK",     2023,  4444,   81.3,             350,       0,
  "Italy",  2023,  3249,   83.7,             0,       0.55,
  "Canada", 2023,  5307,   82.6,             550,       0,
  "Japan",  2023,  4874,   84.7,             500,       0,
  "US",     2023, 10827,   79.3,             0,       0.5
)


data <- 
  read_tsv("https://datawrapper.dwcdn.net/Bxhol/9/dataset.csv") |> 
  mutate(last_year = year == 2023) |>  # this is done because most recent year gets a black fill/border while antecedent years get a white one
  arrange(country, year) |> 
  mutate(
    country_tooltip = if_else(year == 2023, country, paste(country, year, sep = ', '))
  ) |> 
  mutate(
    country_fill = case_when(
      str_detect(country_tooltip, "US") ~ "US",
      str_detect(country_tooltip, "France") ~ "France",
      str_detect(country_tooltip, "Italy") ~ "Italy",
      str_detect(country_tooltip, "Germany") ~ "Germany",
      str_detect(country_tooltip, "Canada") ~ "Canada",
      str_detect(country_tooltip, "Japan") ~ "Japan",
      str_detect(country_tooltip, "UK") ~ "UK",
      TRUE ~ country_tooltip
    )
  ) |> 
  # make sure to hide tooltips for most recent years as they will get an explicit data label there anywyas
  mutate(
    country_tooltip = if_else(year == 2023, '', country_tooltip)
  ) |> 
  mutate(
    country = factor(country, levels = c("Canada", "France", "Germany", "Japan", "Italy", "UK", "US"))
  )

p2 <- data |> 
  ggplot(aes(x = spend, y = le, color = last_year, fill = country_fill, group = country_fill)) +
  geom_point_interactive(
    aes(size = size, data_id = country_fill, tooltip = country_tooltip), 
    shape = 21, alpha = 1
  ) +
  geom_text_interactive(
    data = data |> slice_max(year) |> distinct(country_fill, .keep_all = TRUE), 
    aes(
      text = country_fill, 
      label = country_fill, 
      data_id = country_fill, 
      tooltip = country_tooltip
    ), 
    hjust = -0.3, vjust = 0, alpha = 1
  ) +
  scale_fill_manual(
    breaks = c("US", "France", "Italy", "Germany", "Canada", "Japan", "UK"),
    values = c("#4076A4", "#80B1E2", "#61A961", "#F5C55E", "#FFAEA9", "#DACFC0", "#E94F55")
  ) +
  scale_color_manual(
    breaks = c(FALSE, TRUE),
    values = c('white', 'black')
  ) +
  theme(
    plot.title = element_markdown(size = 12, lineheight = 1.2, linewidth = 1.5),
    plot.subtitle = element_markdown(size = 12, lineheight = 1.2)
  ) + 
  labs(
    title = '**Value for money**',
    subtitle = "How life expectancy and per-capita healthcare spend have changed since 2000.<br> 
               <span style='background-color:#e94f55; color:white; padding:2px 4px;'>**UK**</span> spending is rising, but life expectancy has stalled."
  ) +
  labs(x = NULL, y = NULL) +
  scale_x_continuous(
    breaks = seq(3000, 11000, 1000),
    labels = c(format(seq(3000, 10000, 1000), big.mark = ",", trim = TRUE), "$11,000")
  ) +
  coord_cartesian(
    xlim = c(2100, 11300),
    ylim = c(77, 86), 
    expand = FALSE, 
    clip = 'off'
  ) +
  # add caption for p2
  labs(
    caption = "<span style='color:#232323; font-weight:bold;'>In US Dollars, adjusted for purchasing power and inflation. Excludes 2020-22.</span>  <br>
             <span style='color:#989799; font-weight:bold;'>Chart: Tom Calver | The Times and The Sunday Times</span>"
  ) +
  theme(
    text = element_text(family = 'roboto'), element_text(color = 'black', face = 'bold'),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major = element_line(size = 0.3, color = "grey80"),
    axis.line = element_line(color = "black", size = 0.3),
    legend.position = 'none',
    plot.caption = element_markdown(
      size = 10, 
      hjust = 0, 
      lineheight = 1.2
    )
  ) +
  annotate(
    geom = 'rect', 
    xmin = 2075,
    xmax = 2345,
    ymax = 86.5,
    ymin = 86.15, 
    fill = '#e94f55'
  ) + 
  # we also need to annotate the years 2000 and 2023 with Germany's yellow hex code (to match what Tom has)
  # not so much for Germany but for reference in general to the range of years for the plot
  # 2000 persistent text geom; for 2023 we use text geom; for 2000, we use label with no borders to bring forward '2000'
  annotate( 
    geom = 'label', 
    label = '2000',
    x = 4250,
    y = 77.97,
    color = '#F5C55E',
    fill = 'white',
    label.size = NA,
    fontface = "bold" 
  ) +
  # 2023 persistent text geom
  annotate(
    geom = 'text', 
    label = '2023',
    x = 6400,
    y = 81.2,
    color = '#F5C55E',
    fontface = "bold" 
  ) + 
  # add x and y axes titles (within the plot itself)
  # y axis
  annotate(
    geom = 'text', 
    label = 'Life expectancy',
    x = 2685,
    y = 85.8,
    color = '#7B7B7B',
    fontface = "bold",
    fontfamily = 'Roboto',
    fontsize = 15
  ) +
  # x axis
  annotate(
    geom = 'text', 
    label = 'Per-capita\n  spend',
    x = 11200,
    y = 77.5,
    color = '#7B7B7B',
    fontface = "bold",
    fontfamily = 'Roboto',
    fontsize = 15,
    hjust = .9,
    vjust = .6
  ) 

girafe(
  ggobj = p2,
  width_svg = 10, height_svg = 6, 
  options = list(
    opts_tooltip(
      css = "background: white;
             border: 1px solid #ddd;
             border-radius: 4px;
             padding: 6px;
             font-family: 'Roboto', sans-serif;
             font-size: 14px;
             font-weight: bold;
             color: #232323;
             text-align: left;
             box-shadow: 2px 2px 5px rgba(0, 0, 0, 0.1);"
    ),
    opts_hover(
      css = "stroke-opacity: 1; fill-opacity: 1; color: #232323; font-size: 12px; alpha: 1;"
    ),
    opts_hover_inv(
      css = "fill-opacity: 0.01; stroke-opacity: 0.01; color: transparent; font-size: 0.1px;"
    )
  )
)